To solve all these issues, we can rely on a kind of analysis that can partial out (take away) the variance due to the context. This can be done using multilevel regression analysis (also known as mixed-effect regression). We will not go into the detail of the computations of such highly complex analyses but will simply provide the amount of information necessary to understand and perform the analysis at a basic level. The necessary diagnostic checks are not fully presented here. Simply note that diagnostics for linear regression apply, and that additional diagnostics should be performed, such as checking the normality of residuals at level 2. We will not discuss this further here. The Handbook of multilevel analysis book, edited by De Leeuw and Meijer, provides the necessary information for diagnostics of multilevel models.
When we discussed regression in Chapter 9, Linear Regression, we showed that the value of a criterion attribute for an observation is computed as...
